Enhancing Lung Cancer Detection: A Comparative Analysis of CNN and RNN Models on X-Ray Image Data

Bodicherla Siva Sankar, D Natarajasivan and M Purushotham Reddy
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Bodicherla Siva Sankar: Research Scholar, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, India
D Natarajasivan: Assistant Professor, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, India
M Purushotham Reddy: Professor and Head, Department of Information Technology, Institute of Aeronautical Engineering, India

Biomedical Journal of Scientific & Technical Research, 2024, vol. 58, issue 2, 50150-50179

Abstract: Lung cancer originates from abnormal growth in lung cells, characterized by uncontrolled cell division within lung tissues. Early detection of lung cancer is crucial for improving patient outcomes and survival rates. The cited papers highlight several limitations, such as small sample sizes, reliance on previous studies, the need for future validation, resource-intensive methods, potential biases, lack of longitudinal data, necessity for further experimentation, limited clinical practice integration, dependence on imaging quality, and insufficient data for robust model training and evaluation.

Keywords: Journals on Medical Drug and Therapeutics; Journals on Emergency Medicine; Physical Medicine and Rehabilitation; Journals on Infectious Diseases Addiction Science and Clinical Pathology; Open Access Clinical and Medical Journal; Journals on Biomedical Science; List of Open Access Medical Journal; Journals on Biomedical Engineering; Open Access Medical Journal; Biomedical Science Articles; Journal of Scientific and Technical Research (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:abf:journl:v:58:y:2024:i:2:p:50150-50179

DOI: 10.26717/BJSTR.2024.58.009131

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